GAMA-12 master catalogue

Checks and diagnostics

from herschelhelp_internal import git_version
print("This notebook was run with herschelhelp_internal version: \n{}".format(git_version()))

This notebook was run with herschelhelp_internal version: 
6b6e8f7 (Thu Oct 5 17:35:30 2017 +0100)

%matplotlib inline
#%config InlineBackend.figure_format = 'svg'

import matplotlib.pyplot as plt
plt.rc('figure', figsize=(10, 6))
plt.style.use('ggplot')

import locale
locale.setlocale(locale.LC_ALL, 'en_GB')

import os
import time
import itertools

from astropy.coordinates import SkyCoord
from astropy.table import Table
from astropy import units as u
from astropy import visualization as vis
import numpy as np
from matplotlib_venn import venn3, venn2

from herschelhelp_internal.masterlist import nb_compare_mags, nb_ccplots, nb_histograms

OUT_DIR = os.environ.get('OUT_DIR', "./data")
#SUFFIX = os.environ.get('SUFFIX', time.strftime("_%Y%m%d"))
SUFFIX = "_20171018"

master_catalogue = Table.read("{}/master_catalogue_gama-12{}.fits".format(OUT_DIR, SUFFIX))

I - Summary of wavelength domains

flag_obs = master_catalogue['flag_optnir_obs']
flag_det = master_catalogue['flag_optnir_det']

venn2(
    [
        np.sum(flag_obs == 1),
        np.sum(flag_obs == 2),
        np.sum(flag_obs == 3)
    ],
    set_labels=('Optical', 'near-IR'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/len(flag_obs)))
)
plt.title("Wavelength domain observations");

venn2(
    [
        np.sum(flag_det[flag_obs == 3] == 1),
        np.sum(flag_det[flag_obs == 3] == 2),
        np.sum(flag_det[flag_obs == 3] == 3)
    ],
    set_labels=('near-IR', 'Optical'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/np.sum(flag_det != 0)))
)
plt.title("Detection of the {} sources detected\n in any wavelength domains "
          "(among {} sources)".format(
              locale.format('%d', np.sum(flag_det != 0), grouping=True),
              locale.format('%d', len(flag_det), grouping=True)));

II - Comparing magnitudes in similar filters

The master list if composed of several catalogues containing magnitudes in similar filters on different instruments. We are comparing the magnitudes in these corresponding filters.

u_bands = [           "KIDS u"]
g_bands = ["DECam g", "KIDS g", "SUPRIME g", "GPC1 g"]
r_bands = ["DECam r", "KIDS r", "SUPRIME r", "GPC1 r"]
i_bands = ["DECam i", "KIDS i", "SUPRIME i", "GPC1 i"]
z_bands = ["DECam z",           "SUPRIME z", "GPC1 z"]
y_bands = ["DECam y",           "SUPRIME y", "GPC1 y"]

for band in g_bands:
    print(band, 'max:', np.nanmax(master_catalogue["m_{}".format(band.replace(" ", "_").lower())]))

/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/lib/nanfunctions.py:355: RuntimeWarning: All-NaN axis encountered
  warnings.warn("All-NaN axis encountered", RuntimeWarning)

DECam g max: nan
KIDS g max: 43.5271
SUPRIME g max: nan
GPC1 g max: 28.9741001129

The Suprime g magnitudes are very wrong. They are almost all between 0 and 1.

II.a - Comparing depths

We compare the histograms of the total aperture magnitudes of similar bands.

for bands in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    colnames = ["m_{}".format(band.replace(" ", "_").lower()) for band in bands]
    nb_histograms(master_catalogue, colnames, bands)

HELP warning: the column m_decam_g (DECam g) is empty.
HELP warning: the column m_suprime_g (SUPRIME g) is empty.

HELP warning: the column m_decam_z (DECam z) is empty.

II.b - Comparing magnitudes

We compare one to one each magnitude in similar bands.

for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    for band1, band2 in itertools.combinations(band_of_a_kind, 2):
        
        basecol1, basecol2 = band1.replace(" ", "_").lower(), band2.replace(" ", "_").lower()
        
        col1, col2 = "m_ap_{}".format(basecol1), "m_ap_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (aperture)".format(band1), "{} (aperture)".format(band2)))
        
        col1, col2 = "m_{}".format(basecol1), "m_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (total)".format(band1), "{} (total)".format(band2)))

No sources have both DECam g (aperture) and KIDS g (aperture) values.
No sources have both DECam g (total) and KIDS g (total) values.
No sources have both DECam g (aperture) and SUPRIME g (aperture) values.
No sources have both DECam g (total) and SUPRIME g (total) values.
No sources have both DECam g (aperture) and GPC1 g (aperture) values.
No sources have both DECam g (total) and GPC1 g (total) values.
SUPRIME g (aperture) - KIDS g (aperture):
- Median: -0.10
- Median Absolute Deviation: 0.17
- 1% percentile: -1.7694091796874998
- 99% percentile: 1.1416382789611785

No sources have both KIDS g (total) and SUPRIME g (total) values.
GPC1 g (aperture) - KIDS g (aperture):
- Median: -0.33
- Median Absolute Deviation: 0.33
- 1% percentile: -2.075102996826172
- 99% percentile: 2.269778823852544

GPC1 g (total) - KIDS g (total):
- Median: -0.09
- Median Absolute Deviation: 0.16
- 1% percentile: -1.5102294158935545
- 99% percentile: 0.7436729240417477

GPC1 g (aperture) - SUPRIME g (aperture):
- Median: -0.25
- Median Absolute Deviation: 0.35
- 1% percentile: -2.14129150390625
- 99% percentile: 2.4541178894042948

No sources have both SUPRIME g (total) and GPC1 g (total) values.
KIDS r (aperture) - DECam r (aperture):
- Median: -0.57
- Median Absolute Deviation: 0.42
- 1% percentile: -3.5359573364257812
- 99% percentile: 0.734039306640625

KIDS r (total) - DECam r (total):
- Median: -0.69
- Median Absolute Deviation: 0.43
- 1% percentile: -3.254582099914551
- 99% percentile: 0.992661209106446

SUPRIME r (aperture) - DECam r (aperture):
- Median: -0.61
- Median Absolute Deviation: 0.41
- 1% percentile: -3.591978073120117
- 99% percentile: 0.9319587707519483

SUPRIME r (total) - DECam r (total):
- Median: -0.84
- Median Absolute Deviation: 0.41
- 1% percentile: -3.550533676147461
- 99% percentile: 1.0033042907714531

GPC1 r (aperture) - DECam r (aperture):
- Median: -1.00
- Median Absolute Deviation: 0.37
- 1% percentile: -3.2967313289642335
- 99% percentile: 0.7026295661926285

GPC1 r (total) - DECam r (total):
- Median: -0.86
- Median Absolute Deviation: 0.37
- 1% percentile: -2.5506317138671877
- 99% percentile: 0.4100577354431221

SUPRIME r (aperture) - KIDS r (aperture):
- Median: -0.04
- Median Absolute Deviation: 0.11
- 1% percentile: -0.8898246765136719
- 99% percentile: 0.9822139739990225

SUPRIME r (total) - KIDS r (total):
- Median: -0.21
- Median Absolute Deviation: 0.19
- 1% percentile: -1.4218847274780273
- 99% percentile: 1.2361030578613281

GPC1 r (aperture) - KIDS r (aperture):
- Median: -0.18
- Median Absolute Deviation: 0.23
- 1% percentile: -1.1846766662597656
- 99% percentile: 1.5284150695800705

GPC1 r (total) - KIDS r (total):
- Median: 0.04
- Median Absolute Deviation: 0.12
- 1% percentile: -0.8568764495849609
- 99% percentile: 0.7611067199706989

GPC1 r (aperture) - SUPRIME r (aperture):
- Median: -0.15
- Median Absolute Deviation: 0.24
- 1% percentile: -1.3660277557373046
- 99% percentile: 1.5567139434814363

GPC1 r (total) - SUPRIME r (total):
- Median: 0.14
- Median Absolute Deviation: 0.13
- 1% percentile: -0.8839898109436035
- 99% percentile: 0.8461173057556155

KIDS i (aperture) - DECam i (aperture):
- Median: -0.25
- Median Absolute Deviation: 0.33
- 1% percentile: -2.8722589111328123
- 99% percentile: 1.304483337402342

KIDS i (total) - DECam i (total):
- Median: -0.26
- Median Absolute Deviation: 0.33
- 1% percentile: -1.8559133911132812
- 99% percentile: 2.186717739105226

SUPRIME i (aperture) - DECam i (aperture):
- Median: -0.37
- Median Absolute Deviation: 0.34
- 1% percentile: -3.2666903495788575
- 99% percentile: 0.931794548034671

SUPRIME i (total) - DECam i (total):
- Median: -0.54
- Median Absolute Deviation: 0.31
- 1% percentile: -2.726251163482666
- 99% percentile: 1.2671364402771115

GPC1 i (aperture) - DECam i (aperture):
- Median: -0.46
- Median Absolute Deviation: 0.25
- 1% percentile: -2.445783233642578
- 99% percentile: 0.6057321166992189

GPC1 i (total) - DECam i (total):
- Median: -0.29
- Median Absolute Deviation: 0.25
- 1% percentile: -1.6301992416381834
- 99% percentile: 0.7034828186035158

SUPRIME i (aperture) - KIDS i (aperture):
- Median: -0.08
- Median Absolute Deviation: 0.21
- 1% percentile: -2.0993301010131837
- 99% percentile: 1.3187004470825237

SUPRIME i (total) - KIDS i (total):
- Median: -0.30
- Median Absolute Deviation: 0.30
- 1% percentile: -3.1144363403320314
- 99% percentile: 1.3730266189575193

GPC1 i (aperture) - KIDS i (aperture):
- Median: -0.17
- Median Absolute Deviation: 0.20
- 1% percentile: -1.0249346923828124
- 99% percentile: 0.8876734161376945

GPC1 i (total) - KIDS i (total):
- Median: 0.03
- Median Absolute Deviation: 0.12
- 1% percentile: -0.8408512878417969
- 99% percentile: 0.7019467926025378

GPC1 i (aperture) - SUPRIME i (aperture):
- Median: -0.10
- Median Absolute Deviation: 0.20
- 1% percentile: -1.1620124816894533
- 99% percentile: 0.9892982482910151

GPC1 i (total) - SUPRIME i (total):
- Median: 0.19
- Median Absolute Deviation: 0.11
- 1% percentile: -0.7631856536865234
- 99% percentile: 0.8027851486206053

No sources have both DECam z (aperture) and SUPRIME z (aperture) values.
No sources have both DECam z (total) and SUPRIME z (total) values.
No sources have both DECam z (aperture) and GPC1 z (aperture) values.
No sources have both DECam z (total) and GPC1 z (total) values.
GPC1 z (aperture) - SUPRIME z (aperture):
- Median: -0.08
- Median Absolute Deviation: 0.20
- 1% percentile: -1.0366680145263671
- 99% percentile: 1.1790359497070313

GPC1 z (total) - SUPRIME z (total):
- Median: 0.18
- Median Absolute Deviation: 0.12
- 1% percentile: -0.6331187438964844
- 99% percentile: 0.8256746673583981

SUPRIME y (aperture) - DECam y (aperture):
- Median: -0.05
- Median Absolute Deviation: 0.20
- 1% percentile: -1.866614532470703
- 99% percentile: 1.233930130004885

SUPRIME y (total) - DECam y (total):
- Median: -0.10
- Median Absolute Deviation: 0.16
- 1% percentile: -1.1705833435058595
- 99% percentile: 1.3771499633789048

GPC1 y (aperture) - DECam y (aperture):
- Median: -0.10
- Median Absolute Deviation: 0.18
- 1% percentile: -1.5973902893066405
- 99% percentile: 2.21849609375

GPC1 y (total) - DECam y (total):
- Median: 0.13
- Median Absolute Deviation: 0.15
- 1% percentile: -0.8611082077026367
- 99% percentile: 1.1647806167602512

GPC1 y (aperture) - SUPRIME y (aperture):
- Median: -0.14
- Median Absolute Deviation: 0.28
- 1% percentile: -1.371766586303711
- 99% percentile: 2.32575355529785

GPC1 y (total) - SUPRIME y (total):
- Median: 0.17
- Median Absolute Deviation: 0.16
- 1% percentile: -0.8742595672607422
- 99% percentile: 0.9855241775512698

III - Comparing magnitudes to reference bands

Cross-match the master list to SDSS and 2MASS to compare its magnitudes to SDSS and 2MASS ones.

master_catalogue_coords = SkyCoord(master_catalogue['ra'], master_catalogue['dec'])

III.a - Comparing u, g, r, i, and z bands to SDSS

The catalogue is cross-matched to SDSS-DR13 withing 0.2 arcsecond.

We compare the u, g, r, i, and z magnitudes to those from SDSS using fiberMag for the aperture magnitude and petroMag for the total magnitude.

sdss = Table.read("../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_GAMA-12.fits")
sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)

idx, d2d, _ = sdss_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

sdss = sdss[mask]
ml_sdss_idx = idx[mask]

for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands]:
    for band in band_of_a_kind:
        
        sdss_mag_ap = sdss["fiberMag_{}".format(band[-1])]
        master_cat_mag_ap = master_catalogue["m_ap_{}".format(band.replace(" ", "_").lower())][ml_sdss_idx]
    
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (fiberMag)".format(band[-1]), "{} (aperture)".format(band)))
    
        sdss_mag_tot = sdss["petroMag_{}".format(band[-1])]
        master_cat_mag_tot = master_catalogue["m_ap_{}".format(band.replace(" ", "_").lower())][ml_sdss_idx]
        
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (petroMag)".format(band[-1]), "{} (total)".format(band)))

KIDS u (aperture) - SDSS u (fiberMag):
- Median: 0.10
- Median Absolute Deviation: 0.42
- 1% percentile: -1.6068734741210937
- 99% percentile: 2.954749107360839

KIDS u (total) - SDSS u (petroMag):
- Median: 0.10
- Median Absolute Deviation: 0.42
- 1% percentile: -1.6068734741210937
- 99% percentile: 2.954749107360839

No sources have both SDSS g (fiberMag) and DECam g (aperture) values.
No sources have both SDSS g (petroMag) and DECam g (total) values.
KIDS g (aperture) - SDSS g (fiberMag):
- Median: -0.17
- Median Absolute Deviation: 0.16
- 1% percentile: -0.9890386962890625
- 99% percentile: 1.0339902496337894

KIDS g (total) - SDSS g (petroMag):
- Median: -0.17
- Median Absolute Deviation: 0.16
- 1% percentile: -0.9890386962890625
- 99% percentile: 1.0339902496337894

SUPRIME g (aperture) - SDSS g (fiberMag):
- Median: -0.27
- Median Absolute Deviation: 0.17
- 1% percentile: -1.1210815048217775
- 99% percentile: 1.0015515518188485

SUPRIME g (total) - SDSS g (petroMag):
- Median: -0.27
- Median Absolute Deviation: 0.17
- 1% percentile: -1.1210815048217775
- 99% percentile: 1.0015515518188485

GPC1 g (aperture) - SDSS g (fiberMag):
- Median: -0.52
- Median Absolute Deviation: 0.26
- 1% percentile: -1.8783206939697266
- 99% percentile: 1.920540351867659

GPC1 g (total) - SDSS g (petroMag):
- Median: -0.52
- Median Absolute Deviation: 0.26
- 1% percentile: -1.8783206939697266
- 99% percentile: 1.920540351867659

DECam r (aperture) - SDSS r (fiberMag):
- Median: 0.52
- Median Absolute Deviation: 0.36
- 1% percentile: -0.49354766845703124
- 99% percentile: 2.732038574218751

DECam r (total) - SDSS r (petroMag):
- Median: 0.52
- Median Absolute Deviation: 0.36
- 1% percentile: -0.49354766845703124
- 99% percentile: 2.732038574218751

KIDS r (aperture) - SDSS r (fiberMag):
- Median: -0.23
- Median Absolute Deviation: 0.12
- 1% percentile: -0.8954749298095703
- 99% percentile: 0.4217640686035158

KIDS r (total) - SDSS r (petroMag):
- Median: -0.23
- Median Absolute Deviation: 0.12
- 1% percentile: -0.8954749298095703
- 99% percentile: 0.4217640686035158

SUPRIME r (aperture) - SDSS r (fiberMag):
- Median: -0.26
- Median Absolute Deviation: 0.12
- 1% percentile: -0.951361083984375
- 99% percentile: 0.6180745697021484

SUPRIME r (total) - SDSS r (petroMag):
- Median: -0.26
- Median Absolute Deviation: 0.12
- 1% percentile: -0.951361083984375
- 99% percentile: 0.6180745697021484

GPC1 r (aperture) - SDSS r (fiberMag):
- Median: -0.43
- Median Absolute Deviation: 0.16
- 1% percentile: -1.291998748779297
- 99% percentile: 0.9754924011230486

GPC1 r (total) - SDSS r (petroMag):
- Median: -0.43
- Median Absolute Deviation: 0.16
- 1% percentile: -1.291998748779297
- 99% percentile: 0.9754924011230486

DECam i (aperture) - SDSS i (fiberMag):
- Median: -0.03
- Median Absolute Deviation: 0.21
- 1% percentile: -0.7520022773742676
- 99% percentile: 1.7319891357421873

DECam i (total) - SDSS i (petroMag):
- Median: -0.03
- Median Absolute Deviation: 0.21
- 1% percentile: -0.7520022773742676
- 99% percentile: 1.7319891357421873

KIDS i (aperture) - SDSS i (fiberMag):
- Median: -0.22
- Median Absolute Deviation: 0.12
- 1% percentile: -0.8867145347595214
- 99% percentile: 0.4907330894470201

KIDS i (total) - SDSS i (petroMag):
- Median: -0.22
- Median Absolute Deviation: 0.12
- 1% percentile: -0.8867145347595214
- 99% percentile: 0.4907330894470201

SUPRIME i (aperture) - SDSS i (fiberMag):
- Median: -0.29
- Median Absolute Deviation: 0.12
- 1% percentile: -0.9200366592407226
- 99% percentile: 0.5815175437927244

SUPRIME i (total) - SDSS i (petroMag):
- Median: -0.29
- Median Absolute Deviation: 0.12
- 1% percentile: -0.9200366592407226
- 99% percentile: 0.5815175437927244

GPC1 i (aperture) - SDSS i (fiberMag):
- Median: -0.42
- Median Absolute Deviation: 0.14
- 1% percentile: -1.0596820831298828
- 99% percentile: 0.5549477386474635

GPC1 i (total) - SDSS i (petroMag):
- Median: -0.42
- Median Absolute Deviation: 0.14
- 1% percentile: -1.0596820831298828
- 99% percentile: 0.5549477386474635

No sources have both SDSS z (fiberMag) and DECam z (aperture) values.
No sources have both SDSS z (petroMag) and DECam z (total) values.
SUPRIME z (aperture) - SDSS z (fiberMag):
- Median: -0.17
- Median Absolute Deviation: 0.19
- 1% percentile: -1.1571583557128906
- 99% percentile: 1.0003077316284184

SUPRIME z (total) - SDSS z (petroMag):
- Median: -0.17
- Median Absolute Deviation: 0.19
- 1% percentile: -1.1571583557128906
- 99% percentile: 1.0003077316284184

GPC1 z (aperture) - SDSS z (fiberMag):
- Median: -0.31
- Median Absolute Deviation: 0.18
- 1% percentile: -1.2993665313720701
- 99% percentile: 1.0381328201293956

GPC1 z (total) - SDSS z (petroMag):
- Median: -0.31
- Median Absolute Deviation: 0.18
- 1% percentile: -1.2993665313720701
- 99% percentile: 1.0381328201293956

III.b - Comparing J and K bands to 2MASS

The catalogue is cross-matched to 2MASS-PSC withing 0.2 arcsecond. We compare the UKIDSS total J and K magnitudes to those from 2MASS.

The 2MASS magnitudes are “Vega-like” and we have to convert them to AB magnitudes using the zero points provided on this page:

Band	Fν - 0 mag (Jy)
J	1594
H	1024
Ks	666.7

In addition, UKIDSS uses a K band whereas 2MASS uses a Ks (“short”) band, this page give a correction to convert the K band in a Ks band with the formula:

$$K_{s(2MASS)} = K_{UKIRT} + 0.003 + 0.004 * (J−K)_{UKIRT}$$

# The AB zero point is 3631 Jy
j_2mass_to_ab = 2.5 * np.log10(3631/1595)
k_2mass_to_ab = 2.5 * np.log10(3631/666.7)

twomass = Table.read("../../dmu0/dmu0_2MASS-point-sources/data/2MASS-PSC_GAMA-12.fits")
twomass_coords = SkyCoord(twomass['raj2000'], twomass['dej2000'])

idx, d2d, _ = twomass_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

twomass = twomass[mask]
ml_twomass_idx = idx[mask]

nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_wfcam_j'][ml_twomass_idx],
                labels=("2MASS J", "UKIDSS J (total)"))

UKIDSS J (total) - 2MASS J:
- Median: 0.06
- Median Absolute Deviation: 0.06
- 1% percentile: -0.8233773828031368
- 99% percentile: 0.7282878625581957

ukidss_ks_like = master_catalogue['m_wfcam_k'] + 0.003 + 0.004 * (
    master_catalogue['m_wfcam_j'] - master_catalogue['m_wfcam_k'])
nb_compare_mags(twomass['kmag'] + k_2mass_to_ab, ukidss_ks_like[ml_twomass_idx],
                labels=("2MASS Ks", "UKIDSS Ks-like (total)"))

UKIDSS Ks-like (total) - 2MASS Ks:
- Median: 0.11
- Median Absolute Deviation: 0.10
- 1% percentile: -0.838410065751889
- 99% percentile: 1.102716745084901

nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_vircam_j'][ml_twomass_idx],
                labels=("2MASS J", "VISTA J (total)"))

VISTA J (total) - 2MASS J:
- Median: 0.02
- Median Absolute Deviation: 0.08
- 1% percentile: -1.1603096778013053
- 99% percentile: 1.0473402899263549

nb_compare_mags(twomass['kmag'] + k_2mass_to_ab, master_catalogue['m_vircam_k'][ml_twomass_idx],
                labels=("2MASS Ks", "VISTA Ks (total)"))

VISTA Ks (total) - 2MASS Ks:
- Median: 0.05
- Median Absolute Deviation: 0.14
- 1% percentile: -1.0731929440172698
- 99% percentile: 1.2191779905072637

IV - Comparing aperture magnitudes to total ones.

nb_ccplots(
    master_catalogue['m_suprime_r'],
    master_catalogue['m_ap_suprime_r'] - master_catalogue['m_suprime_r'],
    "r total magnitude (HSC)", "r aperture mag - total mag (HSC)",
    master_catalogue["stellarity"],
    invert_x=True
)

Number of source used: 4366603 / 12369251 (35.30%)

V - Color-color and magnitude-color plots

nb_ccplots(
    master_catalogue['m_suprime_r'] - master_catalogue['m_suprime_i'],
    master_catalogue['m_wfcam_j'] - master_catalogue['m_wfcam_k'],
    "r - i (HSC)", "J - K (UKIDSS)",
    master_catalogue["stellarity"]
)

Number of source used: 37754 / 12369251 (0.31%)

nb_ccplots(
    master_catalogue['m_suprime_i'] - master_catalogue['m_wfcam_j'],
    master_catalogue['m_suprime_r'] - master_catalogue['m_wfcam_k'],
    "WFC i - IRAC1", "r - i (WFC)",
    master_catalogue["stellarity"]
)

Number of source used: 37754 / 12369251 (0.31%)

nb_ccplots(
    master_catalogue['m_wfcam_j'] - master_catalogue['m_wfcam_k'],
    master_catalogue['m_suprime_r'] - master_catalogue['m_wfcam_j'],
    "J - K (UKIDSS)", "r - J (HSC, UKIDSS)",
    master_catalogue["stellarity"]
)

Number of source used: 37757 / 12369251 (0.31%)

nb_ccplots(
    master_catalogue['m_decam_i'] - master_catalogue['m_decam_z'],
    master_catalogue['m_decam_z'] - master_catalogue['m_wfcam_j'],
    "i - z (DECaLS)", "z - J (DECaLS, UKIDSS)",
    master_catalogue["stellarity"]
)

Number of source used: 0 / 12369251 (0.00%)
HELP warning: no sources with observations in both bands